关键词: 粒子群优化, 惯性权值, 粒距, S型函数, 局部开发, 全局探测

Abstract: Concerning the problem that the standard Particle Swarm Optimization (PSO) algorithm in which inertia weight is global parameter, and cannot adapt to the complex and nonlinear optimization process, a Spacing Feedback Inertia Weight (SFIW) was proposed. Taking advantage of the characteristic that Sigmoid function can make the smooth transition between linear and nonlinear, an inertia weight function based on Logistic equation was constructed. In the process of optimization, the nonlinear coefficient of inertia weight function was adjusted according to the particle spacing to make the particle with longer particle spacing get larger inertia weight and make the particle with shorter particle spacing get smaller inertia weight. Therefore, the local exploitation and global exploration get balanced. Finally, the experimental results on several benchmark functions and the comparison with other algorithms show the effectiveness and feasibility of the SFIW-PSO.

Key words: Particle Swarm Optimization (PSO), inertia weight, particle spacing, Sigmoid function, local exploitation, global exploration